Artificial Intelligence in Radiation Therapy : First International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings / edited by Dan Nguyen, Lei Xing, Steve Jiang.

Format:

Book

Contributor:

Nguyen, Dan, Editor.

Xing, Lei, Editor.

Jiang, Steve., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11850

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11850

Language:

English

Subjects (All):

Computer vision.

Artificial intelligence.

Medical informatics.

Computer Vision.

Artificial Intelligence.

Health Informatics.

Local Subjects:

Computer Vision.

Artificial Intelligence.

Health Informatics.

Physical Description:

1 online resource (XI, 172 pages) : 87 illustrations, 74 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life.

Contents:

Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Radiation Therapy

Feasibility of CT-only 3D dose prediction for VMAT prostate plans using deep learning

Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiation Treatment Using Image Saliency

4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network

Toward markerless image-guided radiotherapy using deep learning for prostate cancer

A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN and Weakly Supervised Deep Neural Network

A Novel Deep Learning Framework for Standardizing the Label of OARs in CT

Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery

Voxel-level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions

Online Target Volume Estimation and Prediction From an Interlaced Slice Acquisition - A Manifold Embedding and Learning Approach

One-dimensional convolutional network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Radiation Treatment Planning

Unpaired Synthetic Image Generation in Radiology Using GANs

Deriving lung perfusion directly from CT image using deep convolutional neural network: A preliminary study

Individualized 3D Dose Distribution Prediction Using Deep Learning

Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy

Dose Distribution Prediction for Optimal Treatment of Modern External Beam Radiation Therapy for Nasopharyngeal Carcinoma

DeepMCDose: A Deep Learning Method for Efficient Monte Carlo Beamlet Dose Calculation by Predictive Denoising in MR-Guided Radiotherapy

UC-GAN for MR to CT Image Synthesis

CBCT-based Synthetic MRI Generation for CBCT-guided Adaptive Radiotherapy

Cardio-pulmonary Substructure Segmentation of CT images using Convolutional Neural Networks.

Other Format:

Printed edition:

ISBN:

978-3-030-32486-5

9783030324865

Access Restriction:

Restricted for use by site license.